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The Determinants of Privatised Enterprise Performance in Russia By: Alan A. Bevan, Saul Estrin, Boris Kuznetsov, Mark E. Schaffer, Manuela Angelucci, Julian Fennema and Giovanni Mangiarotti William Davidson Working Paper Number 452 June 2001

William Davidson Institute Working Paper 452

The Determinants of Privatised Enterprise Performance in Russia1

Alan A. Bevan*, Saul Estrin**, Boris Kuznetsov***, Mark E. Schaffer****, Manuela Angelucci**, Julian Fennema**** and Giovanni Mangiarotti****

*European Bank for Reconstruction and Development **Centre for New and Emerging Markets, London Business School, *** Bureau of Economic Analysis, Moscow ****Centre for Economic Reform and Transformation, Heriot-Watt University

First Draft

1

June 2001

Financial support of the UK Department for International Development is gratefully acknowledged. The views expressed in the paper are solely those of the authors.


ABSTRACT

Using data from a large enterprise-level panel designed to address this issue, we account for enterprise performance in Russia. We link performance to four aspects of the economic environment: enterprise ownership; corporate governance; market structures and competition; and financial constraints. We conclude that private ownership and improved performance are not correlated, though restructuring is positively associated with the competitiveness of the market environment. These findings on private ownership support those of previous studies, e.g. Earle and Estrin (1997). Moreover, we find evidence that financially unconstrained firms are better in their undertaking of restructuring measures then financially constrained firms. Further analysis suggests that causality runs from restructuring to financial constraint, rather than the reverse. Finally, our findings indicate strong complementarities between the four factors influencing improved company performance, confirming the view that these factors need to be considered jointly.

JEL classification: D21, L10, P31, G34 Keywords: Privatisation, enterprise performance, competition, corporate governance, investment


NON-TECHNICAL SUMMARY

More than a decade into the transition period, Russia’s economic performance has been disappointing. Compared to leading countries such as Poland, Hungary and the Czech Republic, Russia has been lagging in several indicators of reform success - life expectancy, enterprise restructuring, labour productivity, cumulative GDP growth, and inflation stabilization – despite undergoing the largest privatisation in history. The standard interpretation, based on initial evidence, is that while Russian reformers had successfully changed the ownership structure away from state hands, the emergence and entrenchment of widespread insider privatisation, combined with the lack of development of capital market institutions to exercise ownership discipline, meant that privatisation had little impact on either performance or restructuring. We attempt to shed more light on current understanding of determinants of enterprise performance by examining a more recent data set and by linking variables that have been previously treated separately in the literature. First, we use data from the first large scale random sample conducted in Russia since the mid-1990s on enterprise restructuring. The underlying assumption is that having been collected 5 years after the end of privatisation and almost a decade after the beginning of transition, this data will be more reliable in reflecting the impact of new ownership, incentive and information systems on the performance of companies. Second, we seek to account for enterprise performance by linking it to four key aspects of economic environment outlined in contemporary literature: enterprise ownership; governance; market structures and competition, and financial constraints. Controlling for other factors, we can identify how variation in these four aspects will lead to variation in performance across the observed firms. In general, the findings suggest that restructuring in Russian firms indeed remains modest, and productivity and investment levels – low. Results of analysis of enterprise performance determinants, however, do not confirm standard theoretical hypotheses. We conclude that ownership and performance are not correlated in Russia. It is not evident that outsider ownership


leads to better performance than insider ownership. Our findings do however suggest a positive association between restructuring and competitiveness of the market environment in which firms operate. Moreover, we find evidence of the binding financial constraints that exist for firms of all types, even though larger firms are better at obtaining short-term credit. Financial constraints emerge in our study as a fundamental issue for restructuring in Russian enterprises. There are strong complementarities between these factors. We find that, taken together, domestic monopoly power, financial constraints and to a limited extent state ownership lead to inferior company performance across a wide range of measures. Most strikingly, there are clear interactions between state ownership and market structure. State ownership leads to improved performance across a number of measures when there is moderate domestic competition or import competition. However, state ownership reduces performance when there is domestic monopoly power. Financial constraints also depend on the ownership structure to some extent; they reduce performance across a variety of measures relative to what would pertain in insider owned firms. The results of this work imply that despite the transfer of ownership rights, institutions providing managerial incentives similar to those existing in western economies have not fully evolved in Russia. Hence, our research suggests three major policy conclusions. Firstly, as economic activities of Russian enterprises tend to be restricted to local and regional markets by administrative barriers, we suggest that the state policy should include additional measures limiting regional administrative barriers. Secondly, in view of strong tendencies of integration and cross-ownership in Russian industry, further facilitation of competition is necessary by lowering barriers to foreign competitors. Finally, to alleviate the problem of financing investment, we suggest that the government should promote equity financing by establishing modern corporate governance legislation and practice.


1. Introduction Successful transition in Russia has always been viewed as one of the most important and exacting tasks facing post-communist reformers in Central and Eastern Europe, and their Western advisors. The process of transition was anyway seen to be daunting in relatively more open and reformed economies like Hungary or Poland (see Blanchard et al (1991), Portes et al (1993)). The huge scale and long communist traditions of Russia made obstacles to reform appear almost insurmountable; a view apparently confirmed when the reform process appeared to founder in the face of the August 1998 crash (see EBRD (1999)).

However, reforms in

Russia have included the largest privatisation in history (see Boycko, Shleifer, Vishny (1995)), which transferred ownership in tens of thousands of companies across the country. Initial evidence suggested that, while Russian reformers had successfully changed the ownership structure away from state hands, the emergence and entrenchment of widespread insider privatisation, combined with the lack of development of capital market institutions to exercise ownership discipline, meant that privatisation had little impact on either company performance or restructuring. This finding was deduced in a number of studies during the mid 1990s (e.g. Earle and Estrin (1997), Estrin and Wright (1999) and has become the standard interpretation in a number of important surveys (e.g. Nellis (2000), Djankov and Murrell (2000)). The problem was exacerbated by the low levels of competition in Russia caused by the unfavourable enterprise size distribution bequeathed by planners, large distances and poor transport infrastructure, as well as regional policies (see Brown and Earle (2001)). However, these results were all derived from enterprise surveys undertaken only one or two years after privatisation (1993 – 1996). This was also the period when firms were seeking to recover from disorganisation (see Blanchard and Kremer (1997)). Moreover given the long heritage of communism and planning, they were undertaken too early to deduce definitive conclusions on the impact of the new ownership, incentive and information systems on the performance of companies.2 We were therefore motivated to undertake a second large-scale enterprise survey, based to some extent on the first survey undertaken by the World Bank in

1


1994 (see Commander, Fan and Schaffer (1996)).3 The survey was undertaken through the Bureau of Economic Analysis between mid 1998 – 1999, and was carefully constructed to be a random sample within the population of firms considered. The sample was relatively large – 437 firms – and was confined to six two digit manufacturing industries; to firms employing between 100 and 5000 workers; and to 11 regions within the four main economic zones. Comparison with Goskomstat data confirms that our sample is reasonably representative of the relevant national patterns. Our conceptual framework follows the literature in seeking to link enterprise performance to four key aspects of the economic environment; ownership; governance; market structures and competition, and financial constraints. One can conceive of enterprises pre-reform as operating away from profit maximising equilibrium in two senses. First, levels of output, employment and perhaps capital would exceed those implied by a profit-maximising rule, because of planners preference for giganticism (see e.g. Bennett (1989)). Secondly, firms would be operating well within their production possibility frontiers because of weak managerial incentives. The reform process represents a number of major changes to the economic environment, leading to adjustments in output, inputs, prices and total factor productivity. Once sectoral and regional factors have been controlled for, one can identify the four areas in which variation in the environment will lead to dispersion in the pattern of behaviour.

The first concerns ownership structure. In principle the propositions here are straightforward – private ownership will improve monitoring, help to resolve the principal-agent problem which allows poor managerial performance and sharpen incentives (see Vickers and Yarrow (1995)). As a result, one would expect privately owned firms to perform better than state owned ones – in terms of total factor productivity and therefore profitability, unit costs and financial performance.

The situation was not quite so straightforward in Russia however. Privatisation was mainly to insiders – workers and managers – whose incentives to improve performance were more 2

A point stressed in the papers themselves see e.g. Earle and Estrin (1997); Introduction.

2


ambiguous (see Earle and Estrin (1996)). Indeed, perhaps the fundamental problem in Russia is that privatisation yielded effective control over enterprises to managers, who on average faced dispersed insider (worker) or outsider (investment funds, former workers, banks) owners. However it did not give them sufficient ownership – typically less than 10% of the total stock (see Earle and Estrin (1997)).

In consequence, the incentives to restructure and improve

profitability were frequently outweighed by those to expropriate the assets for themselves (see Stiglitz (1999)). All this implies that one cannot simply compare performance in state and private firms, without also taking into account carefully the ownership structure – insider or outsider, manager or worker, dispersed or concentrated. Moreover, ownership itself is not necessarily the key – control and mechanisms of corporate governance also play a significant part in ensuring private ownership can deliver improved performance. In the survey, we therefore included very detailed information on the structure of ownership, as well as its evolution through time, and on systems for corporate governance, including managerial evaluations of the balance of influences over major enterprise decisions. The third major area that can impact on company performance is the competitiveness of the market environment. In general, even when corporate governance is weak, enterprises can be forced to improve performance by tough competition in their market. This of course depends in part on the effectiveness of the bankruptcy threat, and the monopoly rentals available to the firms (see Nickell (1996)). Several outcomes are feasible. Firms operating in more competitive environments may face pressures to restructure but be unable to find the revenues to do so, while their more monopolistic competitors may be able to finance improved performance. At the end of the day, the direction of the relationship between competitive pressure and company performance is an empirical question, and one which seems likely to interact with ownership structure (see Angelucci et al (2001)).

3

There have been other surveys of Russian firms, in the past three years, on a variety of issues including innovation, corruption, and the new-market economy. None have returned to the fundamental theme of the determinants of company performance, based around a large scale random survey.

3


The role of bankruptcy and the need for funding from profits illustrates our final area of concern – financial constraints. Much of the previous discussion implicitly assumes that capital is available at a fixed interest rate at infinitely elastic supply. In practice in Russia this is not true – capital is scarce and allocated more by rationing than price.

These exogonous financial

constraints can of course restrict restructuring and directly influence total factor productivity and company performance. In this paper, we use the data from our stratified random sample to look separately at the four influences on company performance. In the following section, we introduce our methodology and data set, before considering ownership and corporate governance in the third section. Competition and performance are addressed in the fourth section and the effects of financial constraints in the fifth. The sixth section attempts to bring our findings together by drawing a picture of a “successful” Russian enterprise while conclusions, including interactive effects and policy findings are contained in the seventh section.

2.

Survey Methodology

Our survey was designed to enable the analysis of the relationships between performance, ownership, corporate governance, restructuring, and finance among privatised Russian enterprises. The questionnaire was developed between mid-1998 and end-1999. It was tested in two pilot runs in 1998 and the beginning of 2000 with significant corrections made after the first pilot and some minor changes after the second one. Following this second pilot, the full survey was undertaken in the Spring of 2000. We surveyed 437 enterprises which were randomly sampled from a population list stratified in the manner explained in sub-section 2 below. Given the focus of this study, and the resultant stratification and sampling criteria, our sample was never intended to be representative of Russian industry. Nonetheless, sub-section 3 presents a comparison of the major indicators of our sampled firms with those of the Russian industrial population in order to give some assessment of our sample biases. Sub-section 4 concludes.

4


2.1 Questionnaire and Survey Data The survey data were collected by direct face-to-face interviews with one of the top-managers of the enterprise: in most of the cases the general manager (director/general director) or economic/financial director. 4 The distribution of respondents by position is given in table 2.1 below. However, owing to the detailed nature of the requested quantitative data, this section of the survey was generally collected separately from the accounting or economic department of the enterprise. Table 2.1 Position of the respondent Position of respondent

Freq.

Percent

Cum(%)

Director

189

43.3

43.25

Deputy director

218

49.9

93.14

Other top management

30

6.9

100.00

Total

437

100.00

Source: Authors’ calculations The questionnaire includes six major blocks of questions: •

indicators of economic activity and factors of production (output, capacity and labor utilization, costs, financial in- and outflows, structure of assets, investment activity, etc.);

•

information on restructuring activities of the enterprise (such as shedding of labor, introduction of new technologies, new products, etc.);

•

market structure data (competition, price elasticity, enterprise activity on different geographical markets);

4

In Russia the top manager position may have different names In the ‘Director’ category we include: Director General, Executive Director, Acting Director, Director (if he is the only one with a title). In 3 cases respondents were presidents of the company and in one — the Chairman of the Board. The category ‘Deputy director’ includes Deputies of the top manager and in one case Chief Engineer. In the ‘other’ category there are mostly Heads of Departments (Planning, Economic, etc.)

5


•

ownership and corporate governance data (ownership structure, ownership concentration, board composition, some information on top-management of the enterprise);

•

data on financial constraints (availability of external financing, state assistance, etc.);

•

a block of control variables such as region, industrial code, legal type of enterprise, date and method of privatization and others.

Where feasible, the data was generally collected for the years 1997-1999: hence the sample covered both pre- and post-crisis years. The general principle for composing the questionnaire was to duplicate information regarded as most important; hence e.g., more than one question (quantitative, rank, qualitative) would be included to permit construction of an indicator for main characteristics of the firm5. 2.2 Sampling Strategy From the very beginning the results of this survey were not designed to be representative of the Russian economy nor even of Russian industry. The survey was carried out to enable evaluation of the adjustment of Russian privatised industrial enterprises to new conditions in the transitional economy of the late 1990s. This rationale together with the restricted number of enterprises surveyed (400 enterprises were to be interviewed) led to the sample having some specific features. Moreover the general population of the firms from which the sample was drawn was limited to enterprises in certain industries, size, regions, form of ownership and age (date of establishment of the enterprise). The precise nature of these criteria are explained in detail in the following sub-sections. 2.2.1 Selection of Industries The sample was confined to the population of firms that belong to manufacturing industries according to Russian Industrial Classification (OKONH). The following two-digit industries were selected: 13 - Chemical & Oil-chemical industry;

6


14 - Machinery building & metal-working industry; 15- Wood & Paper industry; 16 – Stone & Clay (Production of building materials) industry; 17 - Light industry; 18 - Food industry. It should be noted that the list does not include the Ferrous and Non-Ferrous metal industries. These were excluded for several reasons. Firstly, as the Russian Industrial Classification does not separate mining and metal production at the two-digit level, the decision to exclude extractive industries precluded the inclusion of metal production. Secondly, the concentration of production in the Ferrous metal industry is extremely high (12 Russian metal plants produce about 90% of ferrous metal; the Non-Ferrous metal sector concentration is lower but still very high). Moreover, metal industry enterprises tend to be large, and hence well above our upper size limit (which is justified below). Consequently it would prove almost impossible to obtain a statistically valid sample for these industries. In addition, as metal plants are unusually export oriented (export share of 80-95 percent), any comparisons between them and other firms would prove rather difficult and uninteresting for our purposes. As a result the industrial stratification was chosen such that the sample would be approximately evenly distributed across our chosen two-digit industries. However, in practice it proved extremely difficult to meet these quotas for some industries, particularly given the need to sample privatised enterprises whilst adhering to the size and regional stratification dimensions. The actual distribution is reported in table 2.2 below, and illustrates that our sample slightly over-represents the machinery sector relative to the industrial population. Nonetheless, there are sufficient observations in each industry to control for industry-specific factors.

5

For some questions where pilot surveys showed a low response rate several options were given to respondent. For example, if it was impossible to get information on separate shares for workers and managers the revised instrument included an option to report the cumulative share of insiders.

7


Table 2.2 Distribution by Industry Industry

Code

Observations

%

Chemicals

13

56

12.8

Machinery

14

108

24.7

Wood

15

66

15.1

Stone&clay

16

72

16.5

Light

17

72

16.5

Food

18

63

14.4

437

100

Total Source: Authors’ Calculations

2.2.2 Size Categories To make our surveyed enterprises more comparable, we restricted the size of our sampled firms to between 100-5000 employees6. Small enterprises with below 100 employees were excluded because: (a) they work under specific tax and accounting rules that often make them incomparable with others; (b) although extremely important for market institutions in the long run — c.f. Poland — they currently account for less that 4 percent of industrial output in Russia, and (c) the Russian SME sector — which has been extensively surveyed for specific SME studies by different researchers in recent years — generally necessitates larger, more specific samples, and was not the focus of this particular study. The upper size limit of 5000 employees was chosen as there are a small number of such big firms in most of the industries, and most have unique features that makes them more suitable for casestudies than for statistical analysis.7 Very large firms in Russia are often located in so called mono-towns, which leads to a very specific type of behaviour and is reflected in their performance. Moreover, very big firms tend to not exist in the same economic environment as

6

Employment was selected as a measure of size in accordance with both Russian legislation for separating SMEs and large enterprises, and common practice. 7 See P.Kuznetsov, A.Muraviev ‘Ownership structure and firm performance in Russia's industrial firms’ for an example of a recent econometric approach to the analysis of very big Russian firms (‘blue chips’).

8


other enterprises, but instead form their own environment to suit their interests: e.g., owing to a special kind of relationship with authorities, natural monopolies, and so on .8 As in the case of the industrial stratification, our aim was to distribute our sample more or less evenly across three broad size categories: 100-500 employees, 501-1000 employees and 10015000 employees. As table 2.3 below illustrates, this stratification was broadly met by the actual sample.

Table 2.3 Distribution by Size in 1999

Selected Industries,

Total

100-500

%

501-1000

%

>1000

%

437

147

33.6%

139

31.8%

151

34.6%

891

265

observations Average size

703

1820

(employees) Source: Authors’ calculations

2.2.3 Selection of Regions Our choice of regions was based on two considerations. The limited number of enterprises to be sampled was insufficient to get representative sub-samples for all or even the majority of regions (oblast, kray, republics) of Russia (89 subjects). On the other hand the regional dimension of the entrepreneurial and investment climates in Russia are acknowledged by most researchers, and hence the regional dimension should be included in any analysis. The palliative solution chosen was to select a limited number of regions belonging to four macro-zones: European Russia, the Volga, the Urals and Siberia. European Russia was represented by two Russian capitals — Moscow and St-Petersburg — together with their respective oblasts (Moscow oblast and StPetersburg oblast); three regions belong to Volga macro-zone — Nizhny Novgorod, Samara, Volgograd; the Ural macro-zone is also represented by three regions — Chelyabinsk, Perm, 8

The initial list of enterprises from which the sample was drawn was based on the Goskomstat Enterprise Registry data included in ALBA-Y database. The registry includes information for about 30,000 medium and large Russian industrial enterprises, accounting for 65-85 percent of output and employment in the selected industries. Utilising this database enabled us to use historical time series data in the analysis and at the same time did not significantly narrow the population of firms to select from.

9


Ekaterinburg (Sverdlovskaya oblast), while the Siberia macro-zone included enterprises from Novosibirsk, Krasnoyarsk and Omsk. We recognize, however, that our approach does not permit reliable analysis of all regional specifics. In particular, regional policies can differ significantly within one macro-zone (Tatarstan, Uliyanovsk and Nizhny Novgorod regions in Volga zone provide good and well known examples). Nevertheless, in many cases the geographical position itself and the distance from the centre are likely to be factors contributing to the macro-zone economic environment and enterprise behavior. Nonetheless, we did not apply strict regional quotas, having decided that for the purposes of this study the size and industrial stratifications were the most important criteria. Indeed we initially aimed to survey in only eight regions across our four macro-zones. One region in each macrozone was ‘reserved’ for use if the size and industry quotas could not be met in two regions. This strategy resulted in some regions (‘reserved’ ones) having a relatively smaller number of observations. Table 2.4 below shows the regional structure of the sample.

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Table 2.4 Regional Distribution Region

No. of Firms

% of Total

122

27.9

Moscow

36

8.2%

Moscow oblast

41

9.4%

St-Peterburg

30

6.9%

St-Peterburg oblast

15

3.4%

Volga macro-zone subtotal

115

26.3%

Nizhny Novgorod oblast

64

14.6%

Samara oblast

39

8.9%

Volgograd oblast

12

2.7%

111

25.4%

Ekaterinburg oblast

50

11.4%

Perm oblast

43

9.8%

Chelyabinsk oblast

18

4.1%

89

20.4%

Krasnoyarsk kray

37

8.5%

Novosibirsk oblast

40

9.2%

Omsk oblast

12

2.7%

437

100.0%

Central macro-zone subtotal

Ural macro-zone subtotal

Siberia macro-zone subtotal

Total Source: Authors’ calculations

2.2.4 Establishment Year and Form of Ownership As our analysis is concerned with post-privatisation behaviour, changes in ownership and corporate governance at former Soviet industrial enterprises, only those enterprises in existence before 1992 (the beginning of transitional reforms in Russia) were eligible for selection. Hence

11


enterprises organized after 1992 as spin-offs of former Soviet ones were excluded. Irrespective of this choice, the vast majority of de novo firms were excluded as a result of the lower size restriction which prevented SMEs from entering the sample. Preliminary analysis showed that the number of big (more than 100 employees) de novo firms in the selected industries and regions in Russia is so small as to preclude the possibility of obtaining a representative sample of such enterprises. Similarly, the focus of our research on the problems and behavioral patterns of privatised enterprises necessitated the exclusion of fully state-owned enterprises that were never privatised. At the same time our population included ‘mixed’ state-private joint-stock companies even in cases where the controlling share of stock belongs to Federal or Regional authorities. 2.3 Does the Sample Represent the Population of Firms? Table 2.5 below compares the results of our sample to the official Goskomstat data for industrial enterprises. From this it is quite evident that our quotas for industries led to certain selection biases: some industries with a smaller number of enterprises (Chemicals and Stone & Clay) are ‘over-represented’ relative to others (Machinery Building, Light and Food industries). This is true not only for the number of enterprises but for employment and sales data as well9. As our enterprises are on average much larger than those in the total population, the average levels of employment and sales are naturally much higher. The most important conclusion comes from comparing the sales to employment ratio for our sample against the population. In general our surveyed enterprises tend to have much higher per capita sales than Russian industrial firms. There are two possible explanations for this: larger 9

The low values for the percentage of surveyed enterprises in the total population are mostly due to the fact that we are comparing our sample with the total enterprise population including SME. We are using the total number of firms just to be able to compare employment and sales coverage ratio (as sales and employment data by size groups is unavailable. In Table A1 the number of observations in the sample is compared to number of medium and large firms in respective industries which provides a better assessment.

12

57776

20323

9528

20784

22263

137842

Machinery

Wood

Stone&clay

Light

Food

Total

437

63

72

72

66

108

56

Survey

Source: Goskomstat, Authors’ calculations

7168

Chemicals

total

Industry

Number of enterprises

0.3

0.3

0.3

0.8

0.3

0.2

0.8

%

13

696445

197848

23522

54945

57109

257076

105945

total

Industry

40818

12267

2238

4189

3015

8829

9053

5.9

6.2

9.5

7.6

5.3

3.4

8.5

%

Industry

9745

1396

888

713

1034

4856

858

total

375

48

50

50

34

123

70

Survey

(thosands)

(mln.Rub) Survey

Number of employees

Sales

Table 2.5 Number of Enterprises, Sales, Employment: Sample vs. Goskomstat Data


3.8

3.4

5.6

7.0

3.3

2.5

8.2

%


enterprises may have higher sales per worker, hence the exclusion of very small firms may have induced a bias in our average, and/or enterprises in our sample tend to be more productive than the average Russian firm in the same industry. Analysis of our data set at the aggregate levelrevealed a positive and highly significant correlation between the sales to employment ratio and size for all three years of our sample.10 However, more detailed analysis revealed that while this is true at the aggregate level, it is not true for every industrial sector. Productivity was found to be highly positively correlated with size in the Food and Wood industries; the correlation was positive but insignificant in the case of the Chemicals and Machinery sectors, while there was an insignificant negative correlation in the case of the Stone & Clay and Light industries. This sectoral heterogeneity suggests that our aggregate bias cannot simply be a function of our size stratification. The results presented in table 2.6 below calculate the profit/sales ratio by industry as a weighted average. Table 2.6 Profitability Comparison 1997 No. Obs.

1998

1999

GKS

Survey

GKS

Survey

GKS

Survey

(%)

(%)

(%)

(%)

(%)

(%)

Chemical

51

2.8

3.7

7.8

9.3

12.5

17.7

Machinery

98

8.0

10.9

10.0

8.9

8.0

11.4

Wood

55

-5.5

9.5

5.0

14.5

11.7

21.3

Stone&clay

64

5.6

9.1

5.2

8.4

4.3

10.2

Light

68

-1.5

1.1

0.9

3.9

4.5

10.9

Food

53

8.4

10.9

12.8

13.0

5.0

10.4

Total Industry

389

7.7

10.4

13.4

Source: Authors’ calculations

10

Using our data set for checking the hypothesis implies that the relationship is the same outside of our 100-5000 employees interval. However, it is not clear that this is true. On the other hand small enterprises in Russia (especially in Food and Light sectors where they can deal with customers in cash) are notorious for being deeply involved into shadow economy and not showing their real output. For 1997 for all industry sales/employment ratio of small enterprises was less than 50% of large and medium sized enterprises.

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From this it can be seen that our bias is generally in favour of more profitable firms. The most prominent differences occur in the Wood & Paper and Stone & Clay sectors. However, the dynamic of the profitability indicator generally corresponds to the national level trend (with the exception of the Stone & Clay sector). Consequently it would appear that while we chose more profitable firms than average in the first instance, their performance trends have been representative of the population. Nonetheless, transition economies in general, and Russia in particular, are notorious for the poor quality of enterprise profits data, as a result of extensive tax evasion. Similarly we are aware that these same biases are likely to appear in our survey data. As the number of observations in industries is not very high, one mistake may lead to significant bias in means. However, if we merely compare the share of profit and loss makers in the sample with the share in the industrial populations, as in table 2.7 below, we confirm the suggestion of a bias in favor of more profitable enterprises.

Table 2.7 Share of Profit-Making Enterprises 1997

1998

1999

GKS

Survey

GKS

Survey

GKS

Survey

(%)

(%)

(%)

(%)

(%)

(%)

Chemical

na

79.2

51.5

75.5

67.7

86.3

Machinery

na

71.0

53.5

72.6

64.8

81.4

Wood

na

59.3

31.9

53.6

49.0

73.2

Stone&clay

na

76.2

43.1

65.6

53.1

73.8

Light

na

63.1

38.2

71.6

50.9

80.6

Food

na

91.3

56.5

77.1

63.4

87.2


In 1997 at the aggregate level 76 percent of the sample were profit makers compared with 53 percent for Russian industry. The corresponding numbers for 1998 and 1999 are 74 percent against 51 percent, and 76 percent against 60 percent in 1999 respectively. The dynamic profit-

15


makers share of the indicator generally corresponds to the national level trend with the exception of Stone & Clay. 2.4 Conclusions From the above discussion we are able to establish several features of our sample: •

On the average the sample is reasonably evenly distributed across size classes and industries. In general the time trend in most cases follow national level patterns.

•

The regional distribution of the sample is approximately even across our four macrozones. However, several regions are underrepresented (Volgograd and Omsk in particular). Consequently any analysis of regional differences should control for this.

•

The average size of the surveyed enterprises is larger than industrial average. This appears to be a result of the initial selection bias and sampling strategy (size quotas).

•

Our sample over-represents better performing enterprises. This can be partially explained (at least for some industries) by the size structure of sample: bigger enterprises are in general more productive. Nonetheless, this bias probably is due to the well-known fact that badly performing enterprises tend to refuse to be surveyed more often than better performing ones.

•

For some industries (Wood & Paper being the most obvious) the sample means and time trends differ significantly with official Goskomstat data.

16

14

15

16

17

18

Machinery

Wood

Stone&clay

Light

Food

63

72

72

66

108

56

437

Survey

Total

1.2

2.4

3.5

2.4

1.7

8.7

2.2

GKS %

Survey/

2421

1071

1231

1188

2694

257

8862

20

28

24

37

29

9

147

Surv

GKS 10772

group1,

Size

group1,

Size

0.8

2.6

1.9

3.1

1.1

3.5

1.7

GKS %

Survey/


** Goskomstat data refers only to medium and large enterprises

17

* Size Group 1: =>100 500 1000 1000

|

68

35

14 |

117

73

48 |

121

Total

|

244

106

38 |

388

244

146 |

390

(if sal>50%ru/obt)


Table 4.7 Ownership, Distribution of Firms by Competition Level |

domestic comp 1=hi 2=med 3=lo

| sopo

import comp. 1=yes

(if sal>50%ru/obt)

0=no

|

1

2

3 |

Total

0

1 |

Total

State owned

|

8

2

1 |

11

10

3 |

13

Private owned

|

205

92

33 |

330

207

124 |

331

|

215

94

34 |

343

217

129 |

346

Total


There is little relationship between the firm's main sector of activity, at the two-digit level, and perceived domestic competition, though this is not true for import competition. Thus from table 4.5 we can observe that around 10 percent of firms in every two-digit sector indicate low levels of domestic competition, except for the machine building and metal working sector where the proportion is closer to 20 percent. However more firms than average in the wood and paper and the food industries report themselves subject to high domestic competition.

In contrast,

significant import competition is relatively lower in these sectors (Stone & Clay and Food), but much higher than average in Light industry and for firms in the Chemical sector.

Interestingly, there is little relationship between perceived market power — domestic or against importers — and enterprise size. This is consistent with the view that perceptions of competition by managers relate to the very specific markets upon which their companies operate, ranging from niche activities for small enterprises to broadly defined markets for much larger ones. 39


However, it is reassuring to note from table 4.6 that, though the differences are slight among larger firms (>1000 employees), there is a relatively higher proportion of enterprises facing low domestic competition and no import competition, while the converse is true for the smallest firm category (5 firms

66

27.27

100

27

29.67

100

19

29.23

100

Total

242

100

91

100

65

100

Domestic Russian competition Bad No.

%

Obs

Good Cum.

No.

%

Obs

%

Very good Cum.

No.

%

Obs

%

Cum. %

no comp.

6

2.67

2.67

6

6.90

6.90

3

4.84

4.84

1 firm

9

4.00

6.67

5

5.75

12.64

2

3.23

8.06

2-5 firms

64

28.44

35.11

20

22.99

35.63

17

27.42

35.48

>5 firms

146

64.89

100

56

64.37

100

40

64.52

100

Total

225

100

87

100

62

100


Secondly we try to evaluate the competitive pressure on each enterprise by calculating the following indicator: Comp_in=r_mshr*comp_r + d_mshr*comp_d

(6.1)

where: r_mshr represents the share of the enterprise’s sales to the regional market; d_mshr represents the share of the enterprise’s sales to the domestic Russian market (i.e. outside of the region the enterprise is located in); comp_r is a dummy variable equal to zero if there less than 2 competitors on the regional market for the enterprise and one otherwise; comp_d is an identically formed dummy for activity on the Russian market. 99


Hence this indicator represents the strength of competition faced by the firm by weighting the competition dummy variables for the regional and domestic Russian markets according to the shares of the relative markets in sales. The group means of this indicator are: bad group 0.72, good 0.67 and very good 0.69, reflecting the slightly higher level of monopoly and duopoly in the good group. The percentage of firms reporting that they face significant import competition is lower in the good and very good groups, though not significantly (37 percent in the bad group relative to 34 percent in the good group and 32 percent in the very good group.%). Within this, it is interesting to note the low number of firms reporting that they face any significant competition from imports. Hypothesis 4. Good enterprises are ‘honest’ ones that show profit. We had no questions directly aimed at measuring shadow activity of the firms. So, two very crude proxy indicators were constructed to try to capture different shadow economy effects.

Firstly, we tried to capture transfer of profits from the enterprise through transfer pricing. To do that we need prices (or at least price indexes) for each enterprise. In other words we need output dynamic in constant and nominal prices. We use the following very simple indicator:

PIndex =

∆Sales (∆K _ U × ∆F _ A)

(6.2)

where: PIndex is the computed price index;

∆Sales represents an index of the growth rate of sales in nominal prices, i.e Salest+1/St; ∆K_U represents a capacity utilization index; ∆F_A represents an index for fixed assets in the balance sheet. We acknowledge that there are several, quite restrictive, assumptions underlying this indicator. For example, we have assumed that the change in capacity utilization should be associated with a proportional increase in output, that the change in fixed assets is reflected by a change in 100


capacity, and we fail to control for any revaluation of fixed assets due to inflation, depreciation and so on Nonetheless, the indicator provides an interesting comparison between our three groups, as illustrated in table 6.11.

Table 6.11 Capacity Utilisation, Fixed Assets and Prices 1999/1998 Bad

1999/1997

No. Obs.

Mean

St. dev

No. Obs.

Mean

St. dev

∆K_U

240

1.19

0.40

237

1.34

1.06

∆F_A

240

0.98

0.28

234

0.92

0.66

∆Sales

243

1.65

0.80

239

1.51

1.39

∆Sales (const. prices)

236

1.16

0.50

228

1.22

1.71

PIndex

235

1.56

0.79

226

1.62

1.10

Good

No. Obs.

Mean

St. dev

No. Obs.

Mean

St. dev

∆K_U

90

1.15

0.29

90

1.29

0.46

∆F_A

88

0.99

0.33

81

0.93

0.40

∆Sales

91

1.77

0.49

84

2.34

1.03

∆Sales (const. prices)

87

1.15

0.50

80

1.22

0.74

PIndex

87

1.75

0.88

80

2.24

1.18

No. Obs.

Mean

St. dev

No. Obs.

Mean

St. dev

∆K_U

66

1.15

0.25

65

1.31

0.78

∆F_A

66

1.16

0.30

65

1.35

0.72

∆Sales

66

1.88

0.54

66

2.64

1.66

∆Sales(const. prices)

66

1.33

0.46

64

1.67

1.07

PIndex

66

1.53

0.59

64

1.87

1.28

Very good


The resulting numbers are not at all as expected. The good group shows higher price growth than the bad and very good groups for both 99/98 and for 99/97 periods; surprisingly, the very good and the bad groups report very similar levels of price growth. It is also interesting to note how differently our groups reacted to the crises. All groups report increased output in constant prices, 101


however, the bad and good groups increased output by about 20 percent between 1997 and 1999, while the very good group increased output by almost 70 percent in the same period. Moreover, the indicators illustrate that while output growth in the very good group was based on expanding capacities — as capacity utilization increased less than output — firms in the good and bad group on average increased output less than capacity utilisation. The second proxy to measure shadow activity was calculated by comparing two answers for the same question: “What is an average wage in your enterprise?” In the interview with the topmanager of the firm was asked to report the average monthly wage of employees at the enterprise. On the other hand we can calculate the average wage from other data by using the balance sheet information collected separately in the accounting department. The results of these question need not coincide if we assume that the enterprise is paying shadow wages out of ‘black-cash’ revenues to save on taxes and social charges. If this is the case, there is a good chance that when asked the general manager would name the real figure as he probably does know how much he had to pay to his workers but may not know the ratio between “white” and “black” cash payments. In this case the difference between the manager’s answer and that based on the wages and salaries reported in the balance sheet data may serve as a measure of the shadow activity. The results of this calculation are shown in the table 6.12 below.

Table 6.12 Deviation in Average Wage by Groups 1998

1999

Mean

Std. Dev.

Mean

Std. Dev.

Bad

1.12

0.20

1.13

0.25

Good

1.11

0.19

1.14

0.23

Very good

1.08

0.18

1.04

0.16


It is encouraging that the very good group has much smaller differential between the manager’s answer and accountants’ figures in 1999, but more careful analysis is needed before any interpretation could be provided. Preliminary analysis showed that this deviation — if it is meaningful at all — does not correlate with performance variables. It is, however, highly

102


significantly correlated with the size of enterprise and is much higher in the Food industry than any other.

7. Conclusions and Meta-Analysis In this paper, we have used the findings from the first large scale random sample conducted in Russian since the mid-1990s on enterprise restructuring to focus on the constraints on microeconomic performance in the period after the 1998 financial crisis. Several key findings emerge from this study. Restructuring in Russian firms remains modest, even nearly a decade after the start of economic reforms. Productivity has continued to fall and remains low, while investment levels are low and restructuring efforts modest. It is, however, encouraging that the bulk of firms are engaged in deep rather than purely defensive restructuring. Under the fairly modest criteria for a “good” firm laid out in section 6 (positive value added and profits and fairly stable output less that 50% of Russian firms in our sample are “good”, and only 16% “very good” in the sense they had also undertaken positive net investment.

The good and very good firms were

characterised by higher capacity utilisation, a younger vintage of capital, less labour hoarding and higher wages and exports.

When we attempt to understand the reasons underlying the wide variation in performance across firms, our results do not confirm standard theoretical hypotheses.

As has been found in

numerous other studies of the former Soviet Union (see e.g. Estrin and Wright (1999) or Djankov and Murrell (2000)) ownership and performance are not well correlated in Russia. In particular, there is no strong evidence that outsider ownership leads to better performance or higher levels of restructuring activity than insider ownership. Conventional explanations point to capital market imperfections, and governance deficiencies (see Nellis (2000)). Our findings are consistent with this in indicating only limited correlation between ownership and perceived control over enterprise decision-making. While insiders are perceived as having control over most insider owned firms, insiders are also perceived to control nearly half of outsider owned firms, and more than a third of state owned firms. This is perhaps a consequence of the high levels of dispersion of outsider ownership in Russia (see Djankov and Murrell (2000)).

103


The findings on competition are slightly more encouraging, in that they confirm a positive association between restructuring activity and the competitiveness of the market environment in which firms operate. Domestic competition spurs more deep restructuring, and to some extent more defensive restructuring as well. Foreign competition is still a relatively insignificant factor in improving enterprise performance in Russia, though it plays more of a role in stimulating investment. However, the results do not yet carry over from qualitative indicators to augmented production functions. On the financial side, our study indicates that company size is a relevant factor in obtaining short but not long term credit, while equity financing is equally rare for all firm types in Russia. Overall, most Russian firms (almost 70%) have quite serious financial constraints because of a combination of limited access to credit, poorly developed capital markets and weak cash flow. Financial constraints appear to be highly correlated with corporate performance and behaviour. However the causality is complex, and given the limited possibility of recourse to external financing from any source runs in large part from restructuring to financial performance rather than the other way round.

Investment is also inversely related to the degree of financial

constraint.

The relationship between the various exogenous variables specifying the factor and product market environment and the internal incentive structures of firms on the one hand, and enterprise performance on the other, are likely to be complex. For example, whether state owned forms perform worse than private ones will depend on both the measure of performance used (profitability as against total factor productivity for example) and the market structure ( state owned firms operating in highly competitive markets may appear to perform relatively better than privately owned firms because bankruptcy constraints bind for the latter but not the former category). Similarly, financial constraints may be more binding for private than state owned firms, especially those operating in competitive markets. To address these issues fully would require another paper. However, here we can usefully bring together the material presented previously by making a first attempt to explore the potential interactions between the independent variables. Rather than use any formal framework, our approach is to estimate equations cross-sectionally for 1999, using the five main measures of 104


company performance employed in this paper; mark-up, return on equity (ROE) return on fixed assets, sales per worker and investment (as a share of fixed assets). We employ as explanatory variables for performance the three main categories of determinant at the heart of this paper – ownership, competition and financial constraints – as well as controls for industry, region and size. The ownership dummy variables are outsider and state (majority) owned firms, and firms with no majority owner, with insider owned firms omitted. The competition dummy variables are medium domestic competition and domestic monopoly (with low competition omitted) and high import competition (low import competition excluded). The financial dummy variables represent partial and serious financial constraints (with the group of firms facing low financial constraints excluded). This yields a total of ten possible interactions, and the results for the five performance measures are reported in Table 7.1. The equations confirm the view that the factors influencing company performance need to be considered together. We observe some clear significant size effects on performance, notably with respect to mark up, ROE and productivity. The equations refute the view that small firms perform better in the Russian context. The broad results from the previous sections of this paper also come through clearly in this more sophisticated framework. Thus taken together we find domestic monopoly power, financial constraints and to a limited extent state ownership lead to inferior company performance across a wide range of measures. However, we can also add some interesting conclusions from the interactions. Most strikingly, there are clear interactions between state ownership and market structure. State ownership leads to improved performance across a number of measures when there is moderate domestic competition or import competition. It reduces it when there is domestic monopoly power. Hence we can confirm market structure effects are more pronounced when we simultaneously control for ownership. Financial constraints also depend on the ownership structure to some extent. Thus, financial constraints reduce performance across a variety of measures relative to what would pertain in insider owned firms. Clearly, more work is needed on these interactive effects, but Table 7.1 indicates strong complementarities between the various factors influencing improved company performance, namely between ownership structure, financial constraints and monopoly power.

105


Table 7.1 Performance on Size, Ownership and Financing Constraints with Interactions Mark-up

Medium Firms

Large Firms

Outside Owned

State Owned

Med.Dom. Comp.

Dom.Monop.

Imp.Comp.

Part. Fin. Const.

Fin.Const.

Return on

Return on

Sales Per

Investment /

Equity

Fixed Assets

Worker

Fixed assets

0.0506**

0.0595*

0.0577

8.8289

0.0104

(0.0219)

(0.0344)

(0.0704)

(14.8023)

(0.0174)

0.0694***

0.0590*

0.0568

40.5450**

0.01622

(0.0225)

(0.0310)

(0.0712)

(15.8802)

(0.0166)

0.0023

-0.0375

0.1030

-1.1472

0.0258

(0.0397)

(0.0591)

(0.1679)

(29.5799)

(0.0425)

0.0113

0.0394

0.6433

48.1440

-0.0966***

(0.0483)

(0.0807)

(0.5237)

(83.2535)

(0.0343)

0.0166

0.0107

-0.0230

-8.7056

0.0311

(0.0281)

(0.0351)

(0.0698)

(16.2156)

(0.0219)

-0.0099

-0.0096

-0.1551*

-38.0353***

-0.0449**

(0.0215)

(0.0967)

(0.0865)

(14.5836)

(0.0188)

0.0042

0.0566*

0.0686

15.1693

0.0318*

(0.0235)

(0.0332)

(0.6778)

(17.0536)

(0.0186)

-0.0080

-0.0694

-0.0966

6.4662

-0.0357

(0.0215)

(0.0489)

(0.0875)

(18.8450)

(0.0249)

-0.0632**

-0.1384***

-0.2612***

-46.7091**

-0.0736***

(0.0282)

(0.0453)

(0.0838)

(18.8050)

(0.0239)

Outside ownership interacted with: Med.Dom. Comp.

Dom.Monop.

Imp.Comp.

0.0002

0.0058

0.1591

14.4245

-0.0477

(0.0476)

(0.0630)

(0.1520)

(25.5738)

(0.0335)

0.0657

0.0116

0.3913

25.3134

0.1204*

(0.0444)

(0.1095)

(0.2665)

(26.7948)

(0.0662)

-0.0005

-0.0712

-0.0267

-28.3582

-0.0160

106


Part. Fin. Const.

Fin.Const.

(0.0430)

(0.0546)

(0.1377)

(25.0633)

(0.0311)

-0.0721*

-0.1173*

-0.4763***

-53.7726*

-0.0432

(0.0367)

(0.0610)

(0.1779)

(31.3683)

(0.0470)

-0.0447

0.0090

-0.1391

-5.1743

-0.0166

(0.0501)

(0.0685)

(0.1948)

(28.4980)

(0.0454)

State ownership interacted with: Med.Dom. Comp.

0.1577**

0.6315***

1.1969***

93.4328*

0.0643

(0.0680)

(0.2362)

(0.4128)

(54.7674)

(0.0592)

-0.4458***

-0.1504

-0.3326

-2.9652

0.0299

(0.1059)

(0.1555)

(0.2859)

(75.7156)

(0.0417)

-0.0467

0.0777

0.7731***

238.2931***

0.0234

(0.0763)

(0.1295)

(0.2816)

(74.4625)

(0.0471)

0.0350

-0.0931

-0.8289

-105.6797

0.0593

(0.0821)

(0.1326)

(0.5808)

(104.2506)

(0.0488)

-0.0310

-0.6206**

-2.2900***

-397.5274***

-0.0185

(0.1235)

(0.2929)

(0.8175)

(131.6609)

(0.0932)

No majority

-0.0036

-0.0335

-0.1089

-19.0423

-0.0060

ownership

(0.0340)

(0.0463)

(0.8581)

(20.6286)

(0.0310)

Constant

0.0839**

0.2867***

0.6236***

387.0141

0.1599***

(0.0362)

(0.0777)

(0.1544)

(37.0041)

(0.0403)

Regional Controls

Yes

Yes

Yes

Yes

Yes

Industry Controls

Yes

Yes

Yes

Yes

Yes

No. obs

306

291

300

310

293

0.2209

0.2118

0.2361

0.4891

0.2108

43.94***

15.95***

28.99***

148.64***

4.6***

Dom.Monop.

Imp.Comp.

Part. Fin. Const.

Fin.Const.

R-squared F

*, ** and *** indicate significance at the 10, 5 and 1 percent levels respectively Source: Authors’ calculations

107

William Davidson Institute Working Paper 452 Overall our research therefore suggests three major policy conclusions. Firstly, this study — in accordance with other studies — demonstrates that Russian enterprises tend to concentrate their activity on the local and regional markets. Though the problem of administrative barriers which limit the ability of enterprises to enter other regional markets is not as acute as might have been expected, many enterprises — in particular those in the food industry — still complain about such barriers. In this regard, we suggest that the state should consider additional measures limiting regional administrative barriers.

Secondly, our results show that further state policy facilitating the development of competition in the Russian industrial markets is a necessity. It appear to be potentially important to lower noneconomic barriers to import and barriers to entry for foreign firms, in view of strong tendencies of integration and cross-ownership in Russian industry.

Finally, we have uncovered strong evidence that self-financing of investments is not a feasible choice for the majority of Russian enterprises due to relatively small profit margins; moreover the inefficiency of banking sector precludes access to alternative appropriate external funding. While equity financing could become an alternative in the future, its development will require more transparent and efficient corporate governance. Currently the average board composition of our sampled enterprises does not appropriately reflect the ownership structure, and does not correspond to standards from other countries: employees, especially management and sometimes regional authorities are over-represented while other groups of stock and stake holders are underrepresented. Hence we suggest that the Government of Russia should enact policy to


establish modern corporate governance legislation and practice, if it wishes to contribute to the development of a robust private sector in the Russian Federation.

109


References Aghion, Philippe and Wendy Carlin. 1996. “Restructuring Outcomes and the Evolution of Ownership Patterns in Central and Eastern Europe.” Economics of Transition. 4:2, pp. 371-388. Angelucci, Manuela et al. 2001. “The Effect of Ownership and Competitive Pressure on Firm Performance in Transition Countries: Micro Evidence from Bulgaria, Romania and Poland.” London: Discussion Paper No.2985, Centre for Economic Policy Research. Bennett, John. 1989. The Economic Theory of Central Planning. Cambridge, Mass.: Blackwell. Bevan, Alan, Saul Estrin and Mark Schaffer. 1999. “Determinants of Enterprise Performance During Transition.” Edinburgh: Discussion Paper No.99/03, Centre for Economic Reform and Transformation, Herriot-Watt University. Bevan, Alan and Jo Danbolt. 2001. “On the Determinants and Dynamics of UK Capital Structure.” London: London Business School, mimeo. Blanchard et al. 1991. Reform in Eastern Europe. Cambridge, Mass.: MIT Press. Blanchard, Olivier and Michael Kremer. 1997. “Disorganization.” Quarterly Journal of Economics. 112:4, pp.1091-1126. Boycko, Maxim, Andrei Shleifer and Robert Vishny. 1995. Privatizing Russia. Cambridge, Mass.: MIT Press. Brown, David and John Earle. 2001. “Privatization, Competition and Reform Strategies: Theory and Evidence from Russian Enterprise Panel Data.” London: Discussion Paper No.2758, Centre for Economic Policy Research. Carlin, Wendy et al. 2001. “Competition and Enterprise Performance in Transition Economies: Evidence from a Cross-Country Survey.” London: Discussion Paper No.2840, Centre for Economic Policy Research. Commander, Simon, Qimiao Fan and Mark Schaffer. 1996. Enterprise Restructuring and Economic Policy in Russia. Washington D.C.: World Bank. Commander, Simon and Christian Mumssen. 1999. “Understanding Barter in Russia.” London: Working Paper No.37, EBRD. Cornelli, Francesca, Richard Portes and Mark Schaffer. 1996. “The Capital Structure of Firms in Central and Eastern Europe,” in Different Paths to a Market Economy: China and European Economies in Transition. Oliver Bouin, Fabrizio Coricelli and Francoise Lemoine, eds. OECD. Djankov, Simeon and Peter Murrell. 2000. “Enterprise Restructuring in Transition: A Quantitative Survey,” forthcoming in Journal of Economic Literature.

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Earle, John and Saul Estrin. 1996. “Employee Ownership in Transition Economies,” in Corporate Governance in Transition. Roman Frydman, Cheryl Gray and Andrzej Rapaczynski, eds. Budapest: Central European University Press. Earle, John and Saul Estrin. 1997. “After Voucher Privatization: The Structure of Corporate Ownership in Russian Manufacturing Industry.” London: Discussion Paper No.1736, Centre for Economic Policy Research. Earle, John and Saul Estrin. 1998. “Privatization, Competition and Budget Constraints: Disciplining Enterprises in Russia.” Stockholm: Working Paper No.128, Stockholm Institute of Transition Economics. EBRD. 1999. Transition Report, 1999: Ten Years of Transition. London: EBRD. Estrin, Saul and Mike Wright. 1999. “Corporate Governance in the Former Soviet Union: An Overview.” Journal of Comparative Economics. 27:3, pp.398-421. Goskomstat data. 1990-1999. Moscow: Goskomstat. Gupta, Nadini, John Ham and Jan Svejnar. 2000. “Priorities and Sequencing in Privatization: Theory and Evidence from the Czech Republic.” The William Davidson Institute and the University of Michigan Business School, mimeo. Kuznetsov, P. and Muraviev, A. “Ownership Structure and Firm Performance in Russia's Industrial Firms.” Moscow: Bureau of Economic Analysis, mimeo. Nellis, John. 2000. “Privatization in Transition Economies: What Happened? What’s Next?” Washington D.C.: World Bank, mimeo. Nickell, Stephen. 1996. “Competition and Corporate Performance.” Journal of Political Economy. 104:4, pp.724-746. Portes, Richard, ed. 1993. Economic Transformation in Central Europe: A Progress Report. London: Centre for Economic Policy Research. Rajan, Raghuram and Luigi Zingales. 1995. “What Do We Know About the Capital Structure? Some Evidence from International Data.” Journal of Finance. 50:5, pp.1421-1460. Stiglitz, Joseph. 1999. “Whither reform? Ten Years of the Transition.” ABCDE Conference. Washington D.C.: World Bank. Vickers, John and George Yarrow. 1995. Privatization: An Economic Analysis. Cambridge Mass.: MIT Press. Walsh, Patrick and Ciara Whelan. 2000. “Firm Performance and the Political Economy of Corporate Governance: Survey Evidence for Bulgaria, Hungary, Slovakia and Slovenia.” Moscow: CEPR/WDI Conference. Willig, Robert. 1987. “Do Entry Conditions Vary Across Markets? Comments and Discussion.” Brookings Papers on Economic Activity. 3:0, pp. 872-876.

111

DAVIDSON INSTITUTE WORKING PAPER SERIES - Most Recent Papers The entire Working Paper Series may be downloaded free of charge at: www.wdi.bus.umich.edu CURRENT AS 4/5/02

Publication No. 452: The Determinants of Privatised Enterprise Performance in Russia

No. 451: Determinants of Financial Distress: What Drives Bankruptcy in a Transition Economy? The Czech Republic Case No. 450: Corporate Governance and the Global Social Void No. 449: Financial Architecture and Economic Performance: International Evidence No. 448: Growth Slowdown Under Central Planning: A Model of Poor Incentives No. 447: Disentangling Treatment Effects of Polish Active Labor Market Policies: Evidence from Matched Samples No. 446: The Impact of Socialist Imprinting and Search for Knowledge on Resource Change: An Empirical Study of Firms in Lithuania No. 445: The Costs, Wealth Effects, and Determinants of International Capital Raising: Evidence from Public Yankee Bonds No. 444: Financial Institutions, Contagious Risks, and Financial Crises No. 443: Banks as Catalysts for Industrialization No. 442: Bank-Based or Market-Based Financial Systems: Which is Better? No. 441: Migration and Regional Adjustment and Asymmetric Shocks in Transition Economies No. 440: Employment and Wages in Enterprises Under Communism and in Transition: Evidence From Central Europe and Russia No. 439: Small business in Russia: A Case Study of St. Petersburg No. 438: Foreign Direct Investment as Technology Transferred: Some Panel Evidence from the Transition Economies No. 437: Whistleblowing, MNC’s and Peace No. 436: A Note on Measuring the Unofficial Economy in the Former Soviet Republics No. 435: The Ownership School vs. the Management School of State Enterprise Reform: Evidence from China No. 434: The Effect of Ownership and Competitive Pressure on Firm Performance in Transition Countries: Micro Evidence from Bulgaria, Romania and Poland. No. 433: The End of Moderate Inflation in Three Transition Economies? No. 432: What Drives the Speed of Job Reallocation During Episodes of Massive Adjustment? No. 431 Forthcoming in: The Journal of Economic Perspectives, “Competition and Corporate Governance in Transition,” 16(2) Feb. 2002. No. 430: Corporate Governance in the Cause of Peace: An Environmental Perspective No. 429: Why do Governments Privatize? No. 428: Testing Russia’s Virtual Economy

Authors Alan A. Bevan, Saul Estrin, Boris Kuznetsov, Mark E. Schaffer, Manuela Angelucci, Julian Fennema and Giovanni Mangiarotti Lubomír Lízal

Date June 2001

Lee A. Tavis Solomon Tadesse

Oct. 2001 Aug. 2001

Zuzana Brixiová and Aleš Bulíř

Mar. 2002

Jochen Kluve, Hartmut Lehmann, and Christoph M. Schmidt Aldas Kriauciunas and Prashant Kale Darius P. Miller and John J. Puthenpurackal Haizhou Huang and Chenggang Xu Marco Da Rin and Thomas Hellmann Ross Levine

Jan. 2002

Jan Fidrmuc

Feb. 2002

Swati Basu, Saul Estrin, and Jan Svejnar Alessandro Kihlgren Nauro F. Campos and Yuko Kinoshita Terry Morehead Dworkin Michael Alexeev and William Pyle David D. Li and Changqi Wu

June 2000

Manuela Angelucci, Saul Estrin, Jozef Konings, and Zbigniew Zolkiewski Josef C. Brada and Ali M. Kutan Stepan Jurajda and Katherine Terrell Saul Estrin

Jan. 2002

Dec. 2001

Don Mayer

Jan. 2002

Loren Brandt, Hongbin Li, and Joanne Roberts Vlad Ivanenko

Dec. 2001

Jan. 2002

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Jan. 2002 Jan. 2002

Dec. 2001